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IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Novel Heuristic Association Pattern Searching Technique For Predicting Type 1 And Type 2 Diabetics

[Full Text]

 

AUTHOR(S)

Gameil s. H. Ali,* , dr.a.nithya

 

KEYWORDS

Diabetic prediction, Type 1, Type 2, Association rule, Association classifier. Heuristic association pattern search

 

ABSTRACT

In this investigation, identification of diabetics using data mining techniques is proposed. Primarily, a novel technique called Heuristic association pattern search [HAPS] has been designed for analysis on diabetic medical dataset. The medical data consists of two types of datasets: Type 1 and Type 2 diabetics. The biological process of these patterns of diabetic datasets was analyzed through heuristic association pattern search. This method improved performance rate of analyzing the biological process and identifying biological changes of medical data and is helpful in extracting appropriate patterns for the cause of diabetics. When comparing to existing method, the proposed method extracts only lesser relevant patterns for each dataset which is the main advantage for escalating the performance rate. The online available diabetic dataset is considered in this investigation, followed by this, patterns from the source data has to be identified and has to be categorized. After categorizing the patterns, Relative risk Patterns are considered using Mining Risk Pattern Set Optimally (MRPSO) process which the essential attributes of the chosen dataset. The parameters such as local support, support, confidence level based on minimum threshold level has to be considered. Association memory and association rule has to be generated for the relative risk patterns with Heuristic Association Rules for Patterns (HAPs).Finally, Type1 and Type2 diabetics are classified using Association classifier by computing the correlation co-efficient. The experimental results show that the proposed HAPS method affords better performance rate in analyzing the biological process and mine relevant patterns of medical data. In this stage, the associative pattern articulating more was selected as the accuracy attained using HAPS is 98.3%. The results were compared with Mining Discriminative Patterns (MDP) and Triple Spectral Clustering (SC3). The results divulge that proposed HAPS ascertains biological association between diabetic types in lesser execution time and provides better pattern quality level based on the significance level.

 

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